OBmap 3D
kanazian
Sep 28, 2020
Goal:
Create a 3D reconstruction of single dimension targeted transcriptomics data in order to calculate the position (not necessarily a specific voxel but moreso a domain-like area) of an ORs two glomeruli.
Prior Steps:
Input TPM data is gencode v25 aligned, RSEM quantified and merged using make_obmtx.Rmd file. Input voxels are based on MRI model which is transformed to 3D coordinates using mri_OB_cubed.Rmd file. Weights are calculated based on the number of voxels in that section for each dimension. Line of glomeruli mirror symmetry was calculated from two-peak AP and ML positions using ob_heatmaps.Rmd file.
Plan:
Use best 2 replicates (1 and 2) to generate the statistical model with 200 iterations (more iter will require running on server or adding more swap space). Model involves ILR transform of dirichlet sampled TPM data which is then used to calculate Bayesian posterior probabilities for each OR in each voxel. p50 quantile value will be used to calculate most likely positions for each OR. To avoid picking a point that is an intersection of high expression sections in each dimension, a clustering function is used to determine high expression areas. A dorsal-ventral constraint function based on prior knowledge of an ORs position in the olfactory epithelium is combined with predicted OB positions in order to ensure 2 matching DV predictions if at least one predicted position matches with olfactory epithelium position.
Packages and functions
knitr::opts_chunk$set(warning=F)
library(MCMCpack)
library(plotly)
library(tidyverse)
library(driver) ## devtools::install_github("jsilve24/driver")
library(Rcpp)
library(ggsci)
library(cowplot)
#detach("package:vegan", unload=TRUE)
# functions -----------------------------------------------------------
# model prep
Sample_data <- function(Y, alpha, iter=1){
d <- array(0, dim=c(N, D, iter))
for (i in 1:nrow(Y)){
d[i,,] <- t(rdirichlet(iter, Y[i,] + alpha[i,]))
}
return(d)
}
# calculate quantiles
sourceCpp("~/Desktop/obmap/r_analysis/3dimOB/input/kzcolQuant.cpp")
#function for distance between 3d points
Dist3d <- function(df, row1, row2) {
thing <- sqrt((df$AntPos[row1] - df$AntPos[row2])^2 +
(df$MedLat[row1] - df$MedLat[row2])^2 +
(df$VenDor[row1] - df$VenDor[row2])^2)
return(thing)
}
# 3d interactive scatterplot
Scat3d <- function(df, ml, ap, vd, color="voxrankperOR") {
plot_ly(df,
x = ml,
y = ap,
z = vd,
color = color,
text = ~paste('Gene:', olfrname,
'<br>Rank:', voxrankperOR,
'<br>p50:', p50),
marker = list(size = 6,
line = list(color = 'black',
width = 0.5)),
type = 'scatter3d',
mode = 'markers') %>%
layout(scene = list(xaxis = list(title = 'Medial-Lateral'),
yaxis = list(title = 'Anterior-Posterior'),
zaxis = list(title = 'Ventral-Dorsal')))
}
#given an OR, pick a number of high probability voxels based on signal to noise ratios
Scat_rank <- function(olfr, topX = 72, chooseOut = "plot", title = NA) {
reranked <- ranked %>%
filter(olfrname == olfr) %>%
mutate(rankofrank = min_rank(voxRankSNR),
rankcol = min_rank(desc(ifelse(rankofrank <= topX, rankofrank, NA))),
isRanked = is.na(rankcol))
besties <- reranked %>% filter(isRanked == 0) %>% arrange(voxRankSNR)
worsties <- reranked %>% filter(isRanked == 1)
all <- bind_rows(besties, worsties)
if (chooseOut == "data") {
return(all)
} else {
p <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=worsties, x=~AntPos, y=~MedLat, z=~VenDor,
color=~rankcol, opacity=0.15,
text = ~paste('Gene:', olfrname,
'<br>voxRankSNR:', voxRankSNR,
'<br>voxSNRdim', voxSNRdim),
marker = list(size = 6)) %>%
add_trace(data=besties, x=~AntPos, y=~MedLat, z=~VenDor, color=~rankcol,
text = ~paste('Gene:', olfrname,
'<br>voxRankSNR:', voxRankSNR,
'<br>voxSNRdim', voxSNRdim),
marker = list(size = 6, line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(p)
} #endif
} #endfunction
#plot p50 values for an olfrs top X voxels
p50plot <- function(olfr, rankx = 50, title = NA) {
bestp50 <- ranked %>%
filter(olfrname == olfr) %>%
mutate(rankp1 = min_rank(p50), rankp = min_rank(desc(rankp1))) %>% filter(rankp <= rankx)
worstp50 <- ranked %>%
filter(olfrname == olfr) %>% mutate(rankp = min_rank(p50)) %>%
filter(rankp > rankx) %>% mutate(rankna = NA)
plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=worstp50, x=~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('Gene:', olfr,
'<br>voxRankSNR:', voxRankSNR,
'<br>voxSNRdim', voxSNRdim),
marker = list(size = 6)) %>%
add_trace(data=bestp50, x=~AntPos, y=~MedLat, z=~VenDor, color=~rankp,
text = ~paste('Gene:', olfr,
'<br>voxRankSNR:', voxRankSNR,
'<br>voxSNRdim', voxSNRdim),
marker = list(size = 6, line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
} #endfunction
#define clusters of best X p50 points using a pairwise matrix and ability to output plots and data
#given a number of top ranking positions for an OR, cluster the points based on spatial position
Cluster <- function (olfr, topX = 72, minClustSize = 5, chooseOut = "plot", title = NA) {
df <- ranked %>%
filter(olfrname == olfr) %>%
mutate(rankofrank = min_rank(desc(min_rank(p50)))) %>%
filter(rankofrank <= topX) %>%
arrange(desc(p50))
cut <- ranked %>%
filter(olfrname == olfr) %>%
mutate(rankofrank = min_rank(desc(min_rank(p50)))) %>%
filter(rankofrank > topX) %>%
arrange(desc(p50))
#make pairwise of matrixes
dmatrix <- matrix(data = 0, nrow = nrow(df), ncol = nrow(df))
for (i in 1:nrow(df)) {
distances <- vector("numeric", length = nrow(df))
toprankvox <- df$voxel[i]
ap <- df$AntPos[i]
ml <- df$MedLat[i]
vd <- df$VenDor[i]
for (j in 1:nrow(df)) {
if (i == j) {
distances[j] <- 0
} else {
distances[j] <- sqrt((df$AntPos[i] - df$AntPos[j])^2 +
(df$MedLat[i] - df$MedLat[j])^2 +
(df$VenDor[i] - df$VenDor[j])^2)
} #endif
} #endforj
neighbors <- which(distances <= sqrt(3))
dmatrix[neighbors, i] <- 1
} #endfori
#cluster matrix
cluster_list <- rep(NA, nrow(df))
for (k in 1:ncol(dmatrix)) {
#skip points that are already in a cluster
if (is.na(cluster_list[k])) {
round <- 1
#do a bunch of rounds, find a way to have it run until it stops finding new points
while (round < topX/3) {
neigh <- which(dmatrix[,k] == 1)
#for each neighbor of previous round of neighbors, find new neighbors
for (l in 1:length(neigh)) {
neigh <- c(neigh, which(dmatrix[,neigh[l]] == 1))
neigh <- neigh[-which(duplicated(neigh))]
neigh <- sort(neigh)
} #endforl
round <- round + 1
} #endwhile
cluster_list[neigh] <- k
} else {
next
} #endif
} #endfork
df_out <- df %>% mutate(rawclust = cluster_list) %>%
group_by(rawclust) %>%
mutate(clustmaxp = max(p50),
clustminp = min(p50),
clustmeanp = mean(p50)) %>%
add_tally() %>%
ungroup() %>%
mutate(clustmaxprank = dense_rank(desc(clustmaxp)),
clustmeanprank = dense_rank(desc(clustmeanp)),
clustsizerank = dense_rank(desc(n))) %>%
arrange(clustsizerank) %>%
mutate(isCluster = ifelse(n >= minClustSize, 1, 0),
clust_unique = dense_rank(desc(clustmaxp * isCluster))) %>%
select(p2.5:ORrankpervox, rankofrank, rawclust:clustsizerank, clust_unique, isCluster)
df_clustered <- df_out %>% filter(isCluster == 1)
too_small <- df_out %>% filter(isCluster == 0.5)
cut_out <- cut %>% mutate(rawclust = NA,
clustmaxp = NA,
clustminp = NA,
clustmeanp = NA,
n = NA,
clustmeanprank = NA,
clustmaxprank = NA,
clustsizerank = NA,
isCluster = 0,
clust_unique = NA) %>%
select(p2.5:ORrankpervox,
rankofrank,
rawclust:clustsizerank,
clust_unique,
isCluster) %>%
bind_rows(too_small)
all_out <- bind_rows(df_clustered, cut_out) %>% unique()
#return various things 'rgb(10,10,10)'
if (chooseOut == "data") {
return(all_out)
} else {
p <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=cut_out, x=~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_p50:', clustmeanp,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique),
marker = list(size = 6)) %>%
add_trace(data=df_out, x=~AntPos, y=~MedLat, z=~VenDor, color=~clust_unique,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_p50:', clustmeanp,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique),
marker = list(size = 6, line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(p)
} #endif
} #endfunction
#run cluster given an incrementing number of voxels to cluster from
#needs to also output some sort of summary statistic to define the optimal number of initial voxels that returns the "best" clusters
BestML <- function(olfr, topMin = 50, topMax = 200, topBy = 25, minSize = 2,
clustersPerHalfBulb = 1, chooseOut = "plot", title = NA) {
cphb <- 0
topStep <- topMin
while (cphb < clustersPerHalfBulb) {
df_in <- Cluster(olfr, topX = topStep, minClustSize = minSize, chooseOut = "data")
df_ml <- df_in %>%
filter(isCluster == 1) %>%
group_by(clust_unique) %>%
mutate(meanML = mean(MedLat),
meanAP = mean(AntPos),
meanVD = mean(VenDor),
side = ifelse(meanML >= symline$mlvals[which(symline$apvals == round(meanAP))],
"Lateral", "Medial")) %>%
ungroup()
df_bestM <- df_ml %>%
filter(side == "Medial") %>%
mutate(MedRank = dense_rank(clust_unique),
LatRank = NA,
TopStep = topStep,
sideRank = MedRank) %>%
filter(MedRank <= clustersPerHalfBulb)
df_bestL <- df_ml %>%
filter(side == "Lateral") %>%
mutate(MedRank = NA,
LatRank = dense_rank(clust_unique),
TopStep = topStep,
sideRank = LatRank) %>%
filter(LatRank <= clustersPerHalfBulb)
clust_bestM <- unique(df_bestM$clust_unique)
clust_bestL <- unique(df_bestL$clust_unique)
#checks and counters
cphb <- min(length(clust_bestM), length(clust_bestL))
topStep <- topStep + topBy
} #endwhile
clust_bestML <- c(clust_bestM, clust_bestL)
which_bestML <- df_in[-which(df_ml$clust_unique %in% clust_bestML),]
df_notbest <- df_in %>%
mutate(sideRank = NA) %>%
select(-clust_unique) %>%
mutate(clust_unique = NA)
df_best <- bind_rows(df_bestM, df_bestL)
df_all <- bind_rows(df_best, df_notbest)
#output
if (chooseOut == "data") {
return(df_all)
} else if (chooseOut == "best") {
return(df_best)
} else if (chooseOut == "notbest") {
return(df_notbest)
} else {
p <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=df_notbest, x=~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_p50:', clustmeanp,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique),
marker = list(size = 6)) %>%
add_trace(data=df_best, x=~AntPos, y=~MedLat, z=~VenDor, color=~clust_unique,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_ML:', meanML,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique,
'<br>SideRank:', sideRank),
marker = list(size = 6, line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(p)
} #endif
} #endfunction
#given a list of Olfr names, output 1 medial and 1 lateral cluster for each name
#perhaps add an if or arg for really big lists to run a reduced step
ListML <- function(x, chooseOut = "plot", title = NA) {
#use a list to build a df of unknown size instead of bind_row each iteration
list_out <- vector("list", length = length(x))
for (i in 1:length(x)) {
list_out[[i]] <- BestML(x[i], topMin = 50, topMax = 150, topBy = 50,
minSize = 2, clustersPerHalfBulb = 1, chooseOut = "best")
} #endfor
#always need notbest for the shape shell1
notbest <- BestML(x[1], topMin = 100, topMax = 150, topBy = 50, minSize = 2,
clustersPerHalfBulb = 1, chooseOut = "notbest")
df_out <- bind_rows(list_out)
#output
if (chooseOut == "data") {
return(df_out)
} else if (chooseOut == "point") {
df_point <- df_out %>% filter(p50 == clustmaxp)
p <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=notbest, x=~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_p50:', clustmeanp,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique),
marker = list(size = 6)) %>%
add_trace(data=df_point, x=~AntPos, y=~MedLat, z=~VenDor, color=~olfrname,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_ML:', meanML,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique,
'<br>SideRank:', sideRank),
marker = list(size = 6, line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(p)
} else {
p <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=notbest, x=~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_p50:', clustmeanp,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique),
marker = list(size = 6)) %>%
add_trace(data=df_out, x=~AntPos, y=~MedLat, z=~VenDor, color=~olfrname,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_ML:', meanML,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique,
'<br>SideRank:', sideRank),
marker = list(size = 6, line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(p)
} #endif
} #endfunction
#run Cluster and check if either top ranked M or L glom is dorsal and has known dorsal expression or is class 1. If True, find a dorsal glom for both M and L
DorsalML <- function(olfr, topMin = 100, topBy = 100, minSize = 2,
clustIn = 5, clustOut = 1, chooseOut = "plot",
title = NA) {
print(olfr)
clustFound <- 0
topStep <- topMin
while (clustFound != clustOut) {
df_in <- Cluster(olfr, topX = topStep, minClustSize = minSize, chooseOut = "data")
df_ml <- df_in %>%
filter(isCluster == 1) %>%
group_by(clust_unique) %>%
mutate(meanML = mean(MedLat),
meanAP = mean(AntPos),
meanVD = mean(VenDor)) %>%
ungroup() %>%
rowwise() %>%
mutate(side = ifelse(meanML >= symline$mlvals[which(symline$apvals == round(meanAP))],
"Lateral", "Medial")) %>%
ungroup() %>%
left_join(info, by = "olfrname") %>%
rowwise() %>%
mutate(dorsalRating = sum(ifelse(class == 1, 2, 0),
ifelse(tzsimple < 2, 1, 0),
ifelse(oe_region == "Dorsal", 1, 0),
ifelse(str_detect(olfrname, "Olfr"), 0, 2),
na.rm = T)) %>%
ungroup()
df_bestM <- df_ml %>%
filter(side == "Medial") %>%
mutate(MedRank = dense_rank(clust_unique),
LatRank = NA,
TopStep = topStep,
minSize = minSize,
clustIn = clustIn,
sideRank = MedRank) %>%
filter(MedRank <= clustIn) %>%
arrange(MedRank)
df_bestL <- df_ml %>%
filter(side == "Lateral") %>%
mutate(MedRank = NA,
LatRank = dense_rank(clust_unique),
TopStep = topStep,
minSize = minSize,
clustIn = clustIn,
sideRank = LatRank) %>%
filter(LatRank <= clustIn) %>%
arrange(LatRank)
max <- max(c(df_bestM$meanVD[1], df_bestL$meanVD[1]))
min <- min(c(df_bestM$meanVD[1], df_bestL$meanVD[1]))
#would be nice to remake using dplyr::case_when()
if (is.na(max)) {
print(paste("NA", as.character(topStep)))
topStep <- topStep + topBy
next
} else {
if (max >= 13) {
if (min < 13) {
if (df_bestM$dorsalRating[1] >= 2) {
#OR has dorsal info, constrain both med/lat to have dorsal glom
if (df_bestM$meanVD[1] == max) {
#Medial was dorsal, find dorsal lateral glom and viceversa
df_bestM <- df_bestM %>%
filter(MedRank == 1) %>% mutate(test = "1m")
df_bestL <- df_bestL %>%
filter(meanVD >= 13) %>%
filter(LatRank == min(LatRank)) %>% mutate(test = "1m")
} else if (df_bestL$meanVD[1] == max) {
df_bestL <- df_bestL %>%
filter(LatRank == 1) %>% mutate(test = "1l")
df_bestM <- df_bestM %>%
filter(meanVD >= 13) %>%
filter(MedRank == min(MedRank)) %>% mutate(test = "1l")
} #endif df_bestM$meanVD[1] == max
} else if (df_bestM$dorsalRating[1] == 1) {
#OR has mixed info, return best
df_bestM <- df_bestM %>% filter(MedRank == 1) %>% mutate(test = "2")
df_bestL <- df_bestL %>% filter(LatRank == 1) %>% mutate(test = "2")
} else {
#OR has ventral info, constrain both med/lat to have ventral glom
if (df_bestM$meanVD[1] == min) {
#Medial was ventral, find ventral ateral glom and viceversa
df_bestM <- df_bestM %>%
filter(MedRank == 1) %>% mutate(test = "3m")
df_bestL <- df_bestL %>%
filter(meanVD < 13) %>%
filter(LatRank == min(LatRank)) %>% mutate(test = "3m")
} else if (df_bestL$meanVD[1] == min) {
df_bestL <- df_bestL %>%
filter(LatRank == 1) %>% mutate(test = "3l")
df_bestM <- df_bestM %>%
filter(meanVD < 13) %>%
filter(MedRank == min(MedRank)) %>% mutate(test = "3l")
} #endif (df_bestM$meanVD[1] == min)
} #endif (df_bestM$dorsalRating[1] >= 2)
} #endif (min < 13)
} else {
#both gloms are ventral, check if dorsal info
if (df_bestM$dorsalRating[1] >= 2) {
#both gloms are ventral, but OR has dorsal info, constrain to dorsal
df_bestM <- df_bestM %>%
filter(meanVD >= 13) %>%
filter(MedRank == min(MedRank)) %>% mutate(test = "4")
df_bestL <- df_bestL %>%
filter(meanVD >= 13) %>%
filter(LatRank == min(LatRank)) %>% mutate(test = "4")
} else {
df_bestM <- df_bestM %>% filter(MedRank == 1) %>% mutate(test = "5")
df_bestL <- df_bestL %>% filter(LatRank == 1) %>% mutate(test = "5")
} #endif (df_bestM$dorsalRating[1] >= 2)
} #endif (max >= 13)
} #endif (is.nax(max))
df_bestM <- df_bestM %>% filter(MedRank == min(MedRank)) %>% mutate(test = "suck")
df_bestL <- df_bestL %>% filter(LatRank == min(LatRank)) %>% mutate(test = "suck")
clust_bestM <- unique(df_bestM$clust_unique)
clust_bestL <- unique(df_bestL$clust_unique)
#checks and counters
clustFound <- min(c(length(clust_bestM), length(clust_bestL)))
print(topStep)
topStep <- topStep + topBy
clustIn <- round(clustIn * 1.5)
} #endwhile
clust_bestML <- c(clust_bestM, clust_bestL)
which_bestML <- df_in[-which(df_ml$clust_unique %in% clust_bestML),]
df_best <- bind_rows(df_bestM, df_bestL) %>% unique()
df_notbest <- df_in %>%
mutate(sideRank = NA) %>%
select(-clust_unique) %>%
mutate(clust_unique = NA) %>%
unique()
df_all <- bind_rows(df_best, df_notbest) %>% unique()
#output
if (chooseOut == "data") {
return(df_all)
} else if (chooseOut == "best") {
return(df_best)
} else if (chooseOut == "notbest") {
return(df_notbest)
} else {
p <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=df_notbest, x=~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_p50:', clustmeanp,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique),
marker = list(size = 6)) %>%
add_trace(data=df_best, x=~AntPos, y=~MedLat, z=~VenDor, color=~clust_unique,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_ML:', meanML,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique,
'<br>SideRank:', sideRank),
marker = list(size = 6, line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(p)
} #endif
} #endfunction
#given a list of Olfr names, output 1 medial and 1 lateral cluster for each name
ListDorML <- function(x, chooseOut = "plot", title = NA) {
#use a list to build a df of unknown size instead of bind_row each iteration
list_out <- vector("list", length = length(x))
for (i in 1:length(x)) {
list_out[[i]] <- DorsalML(x[i], topBy = 200, chooseOut = "best")
} #endfor
#always need notbest for the shape shell1
notbest <- DorsalML(x[1], chooseOut = "notbest")
df_out <- bind_rows(list_out)
#output
if (chooseOut == "data") {
return(df_out)
} else if (chooseOut == "point") {
df_point <- df_out %>% filter(p50 == clustmaxp)
p <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=notbest, x=~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_p50:', clustmeanp,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique),
marker = list(size = 6)) %>%
add_trace(data=df_point, x=~AntPos, y=~MedLat, z=~VenDor, color=~olfrname,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_ML:', meanML,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique,
'<br>SideRank:', sideRank),
marker = list(size = 6, line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(p)
} else {
p <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=notbest, x=~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_p50:', clustmeanp,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique),
marker = list(size = 6)) %>%
add_trace(data=df_out, x=~AntPos, y=~MedLat, z=~VenDor, color=~olfrname,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_ML:', meanML,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique,
'<br>SideRank:', sideRank),
marker = list(size = 6, line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(p)
} #endif
} #endfunction
#using heatmap peak data, create a 3d raw point or find nearest blank OB shell point to that raw point, options to use tan zone to assign a VD position or use average of VD peaks
Heat3D <- function(olfr, dimrep = 1, dv = "oe", raw = F, chooseOut = "plot") {
df_in <- heatmap_peaks %>% filter(olfrname %in% olfr) %>% arrange(side)
if (nrow(df_in) == 0) {
print(paste("No", olfr, "Found"))
next
} #endif
if (dv == "oe") {
df_dv <- df_in %>%
mutate(VD_selected = VDtz,
VD_value = "Tan Based")
} else {
df_dv <- df_in %>%
mutate(VD_selected = VDavg,
VD_value = "Avg Based")
} #endif
if (dimrep == 1) {
df_out <- df_dv %>%
select(olfrname, AntPos10, APval10, MedLat8, MLval8, VD_selected, VD_value, side) %>%
rename(AP_selected = AntPos10, AP_value = APval10,
ML_selected = MedLat8, ML_value = MLval8)
} else if (dimrep == 2) {
df_out <- df_dv %>%
select(olfrname, AntPos13, APval13, MedLat11, MLval11, VD_selected, VD_value, side) %>%
rename(AP_selected = AntPos13, AP_value = APval13,
ML_selected = MedLat11, ML_value = MLval11)
} else if (dimrep == 3) {
df_out <- df_dv %>%
select(olfrname, AntPos15, APval15, MedLat16, MLval16, VD_selected, VD_value, side) %>%
rename(AP_selected = AntPos15, AP_value = APval15,
ML_selected = MedLat16, ML_value = MLval16)
} else if (dimrep == "123") {
df_out <- df_dv %>% rowwise() %>%
mutate(AP_selected = mean(c(AntPos10, AntPos13, AntPos15)),
AP_value = mean(c(APval10, APval13, APval15)),
ML_selected = mean(c(MedLat8, MedLat11, MedLat16)),
ML_value = mean(c(MLval8, MLval11, MLval16))) %>%
ungroup()
} else if (dimrep == "12") {
df_out <- df_dv %>% rowwise() %>%
mutate(AP_selected = mean(c(AntPos10, AntPos13)),
AP_value = mean(c(APval10, APval13)),
ML_selected = mean(c(MedLat8, MedLat11)),
ML_value = mean(c(MLval8, MLval11))) %>%
ungroup()
} #endif
if (raw == T) {
if (chooseOut == "plot") {
plot <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data = blankdata,
x = ~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('AntPos:', AntPos,
'<br>MedLat:', MedLat,
'<br>VenDor:', VenDor),
marker = list(size = 6)) %>%
add_trace(data = df_out,
x = ~AP_selected,
y = ~ML_selected,
z = ~VD_selected,
color = ~olfrname,
text = ~paste('Gene:', olfrname,
'<br>AntPos:', AP_selected,
'<br>APvalue', AP_value,
'<br>MedLat:', ML_selected,
'<br>MLvalue:', ML_value,
'<br>VenDor:', VD_selected,
'<br>VDvalue:', VD_value,
'<br>Side:', side,
'<br>Point:', "Raw"),
marker = list(size = 6,
line = list(color='black', width = 0.5))) %>%
layout(scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(plot)
} else if (chooseOut == "data") {
return(df_out)
}
} else if (raw == F) {
df_close <- tibble("AntPos" = numeric(), "MedLat" = numeric(), "VenDor" = numeric())
for (i in 1:nrow(df_out)) {
distances <- vector(mode = "numeric", length = nrow(blankdata))
sidevec <- c("Lateral", "Medial")
for (j in 1:nrow(blankdata)) {
distances[j] <- sqrt((df_out$AP_selected[i] - blankdata$AntPos[j])^2 +
(df_out$ML_selected[i] - blankdata$MedLat[j])^2 +
(df_out$VD_selected[i] - blankdata$VenDor[j])^2)
df_close[i,] <- blankdata[which(rank(distances, ties.method = "first") == 1),]
}
}
df_close_out <- df_close %>%
mutate(side = sidevec) %>%
left_join(df_out, by = "side") %>%
select(olfrname, AntPos, VenDor, MedLat, side, everything())
if (chooseOut == "plot") {
plot <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data = blankdata,
x = ~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('AntPos:', AntPos,
'<br>MedLat:', MedLat,
'<br>VenDor:', VenDor),
marker = list(size = 6)) %>%
add_trace(data = df_close_out,
x = ~AntPos,
y = ~MedLat,
z = ~VenDor,
color = ~olfrname,
text = ~paste('Gene:', olfrname,
'<br>AntPos:', AP_selected,
'<br>APvalue', AP_value,
'<br>MedLat:', ML_selected,
'<br>MLvalue:', ML_value,
'<br>VenDor:', VD_selected,
'<br>VDvalue:', VD_value,
'<br>Side:', side,
'<br>Point:', "Nearest Shell"),
marker = list(size = 6,
line = list(color='black', width = 0.5))) %>%
layout(scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(plot)
} else if (chooseOut == "data") {
return(df_close_out)
} #endif
} #endif
} #endfunction
#compute distances between heatmap peak point and good point data featuring eurovision movie memes
DistHeat3D <- function(olfr_list, heat_dimrep = 1, heat_dv = "oe", heat_raw = F) {
doubletrouble <- tibble(olfrname = character(), side = character(),
distance = numeric(), ap_ml_vd_3d = character(),
ap_ml_vd_heat = character(), .rows = 0)
for (i in 1:length(olfr_list)) {
heatpoint <- Heat3D(olfr_list[i], dimrep = heat_dimrep,
dv = heat_dv, raw = heat_raw, chooseOut = "data") %>%
select(olfrname, AntPos, MedLat, VenDor, side) %>%
mutate(origin = "heat",
ap_ml_vd_heat = paste(AntPos, MedLat, VenDor,sep = "_"))
goodpoint <- good_point %>%
filter(olfrname == olfr_list[i]) %>%
select(olfrname, AntPos, MedLat, VenDor, side) %>%
mutate(origin = "3d",
ap_ml_vd_3d = paste(AntPos, MedLat, VenDor,sep = "_"))
doubletrouble <- bind_rows(doubletrouble,
left_join(heatpoint, goodpoint,
by = c("olfrname", "side"),
suffix = c("_h", "_3")) %>%
mutate(distance = sqrt((AntPos_h - AntPos_3)^2 +
(MedLat_h - MedLat_3)^2 +
(VenDor_h - VenDor_3)^2)) %>%
select(olfrname, side, distance,
ap_ml_vd_3d, ap_ml_vd_heat))
} #endfor
doubletrouble <- doubletrouble %>%
mutate(dimrep = heat_dimrep,
vd_assignment = heat_dv,
use_raw = heat_raw)
return(doubletrouble)
} #endfunctionBuild model
Input: TPM (gene length and sequence depth normalized expression unit) for 1088 intact ORs across 269 samples across 6 Anterior-Posterior, 3 Medial-Lteral, and 3 Ventral-Dorsal 100um vibratome sliced single OBs.
kzY <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/input/covarintactchemo_over_samples_200923.csv", col_names = TRUE) %>% select(-X1) %>% filter(dimrep == 1 | dimrep == 2)
ectopic <- c("Olfr287","Olfr32")
kzYgood <- dplyr::select(kzY, -ectopic) %>% dplyr::select(-name, -rep, -slice, -dim, -dimrep)
dim(kzYgood) #1100 minus number of genes in ectopic
kzmY <- as.matrix(kzYgood)
rownames(kzmY) <- paste0("sample", 1:dim(kzY)[1])
#rdirichlet requires vector not dataframe so matrix the tibble
kzX <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/input/allmice_covariates_trim_voxweights_v3.csv") %>% filter(name %in% kzY$name)
N <- dim(kzmY)[1] #number of sections
D <- dim(kzmY)[2] #number of genes
# Voxel System
d1 <- 23 #antpost
d2 <- 22 #medlat
d3 <- 23 #vendor
#load voxels from bulb_in_cube
# voxalls_test <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/input/blankOBcoords200922.csv") %>% mutate(type = "latfill") #incorporating fill for 1 voxel thick layer on most lateral surface
# voxalls_0 <- readRDS("~/Desktop/obmap/r_analysis/3dimOB/input/191010_outer_inner_coords_ml23nohole.RDS")
# voxalls_0 <- bind_rows(voxalls_0, voxalls_test)
# voxalls_1 <- voxalls_0[-which(duplicated(voxalls_0)),]
# vox_ap <- voxalls_1[which(voxalls_1$AntPos <= d1),]
# vox_ml <- vox_ap[which(vox_ap$MedLat <= d2),]
# vox_vd <- vox_ml[which(vox_ml$VenDor <= d3),]
# voxalls <- vox_vd %>% select(-type) %>% unique()
voxalls <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/input/voxalls_200924.csv")
# voxweights <- voxalls %>%
# group_by(AntPos) %>%
# mutate(ap.cnt = n(), ap.weight = ap.cnt/dim(voxalls)[1]) %>%
# ungroup() %>%
# group_by(VenDor) %>%
# mutate(vd.cnt = n(), vd.weight = vd.cnt/dim(voxalls)[1]) %>%
# ungroup() %>%
# group_by(MedLat) %>%
# mutate(ml.cnt = n(), ml.weight = ml.cnt/dim(voxalls)[1]) %>%
# ungroup() %>%
# mutate(voxweight = ap.weight*vd.weight*ml.weight)
#
# vw_ap <- voxalls %>%
# group_by(AntPos) %>%
# summarise(ap.cnt = n(), ap.weight = ap.cnt/dim(voxalls)[1], dim = "AntPos") %>%
# rename(slice = AntPos, count = ap.cnt, weight = ap.weight)
# vw_ml <- voxalls %>%
# group_by(MedLat) %>%
# summarise(ml.cnt = n(), ml.weight = ml.cnt/dim(voxalls)[1], dim = "MedLat") %>%
# rename(slice = MedLat, count = ml.cnt, weight = ml.weight)
# vw_vd <- voxalls %>%
# group_by(VenDor) %>%
# summarise(vd.cnt = n(), vd.weight = vd.cnt/dim(voxalls)[1], dim = "VenDor") %>%
# rename(slice = VenDor, count = vd.cnt, weight = vd.weight)
# voxweights <- bind_rows(vw_ml, vw_vd, vw_ap)
#write_csv(voxweights, "~/Desktop/obmap/r_analysis/3dimOB/input/voxweights_200923.csv")
voxweights <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/input/voxweights_200923.csv")
# pick prior and Monte Carlo Sample
alpha <- matrix(0.65, N, D)
iter <- 200
pi_post <- Sample_data(kzmY, alpha, iter)
check <- ifelse(t(voxalls[,kzX$dim]) == kzX$slice, T, F)
weights <- ifelse(check, kzX$weight, 0)
weights <- miniclo(t(weights))
colnames(weights) <- rownames(kzmY)
# We are going to use the ILR basis for computation
contrast.matrix <- create_default_ilr_base(D)
eta_post <- ilr_array(pi_post, contrast.matrix, 2)
# now calculate weighted composition
w8_start <- Sys.time()
q_ilr <- array(0, dim=c(nrow(voxalls), D-1, iter))
for (i in 1:iter) q_ilr[,,i] <- weights %*% eta_post[,,i] #matrix multiplication operator
w8_end <- Sys.time()
(w8_time <- w8_end - w8_start)
#clear RAM
remove(pi_post)
remove(eta_post)
gc()
#proportional composition rather than in ILR, using driver 18-08-16 new ilrInv_array with transposed q_ilr and coord == 1
propstart <- Sys.time()
ilr_prop <- aperm(q_ilr, c(2,1,3))
remove(q_ilr)
gc()
q_proportions <- ilrInv_array(ilr_prop, contrast.matrix, 1)
propend <- Sys.time()
(proptime <- propend - propstart)
remove(ilr_prop)
remove(contrast.matrix)
gc()
sumstr <- Sys.time()
q_proptidy <- aperm(q_proportions, c(3,2,1))
d <- dim(q_proptidy)
q_proptidy <- matrix(q_proptidy, d[1], prod(d[-1]))
quantiles <- c(0.025, .25, .5, .75, .975)
q_prop_quantiles <- t(kzcolQuant(q_proptidy, quantiles))
colnames(q_prop_quantiles) <- paste0("p", 100*quantiles)
voxel=rep(1:d[2], times=D)
coord.ap = voxalls[voxel, "AntPos"]
coord.ml = voxalls[voxel, "MedLat"]
coord.vd = voxalls[voxel, "VenDor"]
tidy_result <- data.frame(voxel=rep(1:d[2], times=D),
gene=rep(1:d[3], each=nrow(voxalls)),
q_prop_quantiles) %>%
bind_cols(coord.ap, coord.ml, coord.vd)
head(tidy_result)
sumend <- Sys.time()
(sumtime <- sumend - sumstr)
saveRDS(tidy_result, file = "~/Desktop/obmap/r_analysis/3dimOB/tidyresults/200i_allchemo_dimrep12_allvox.RDS")Load info and features
Both sexes represented in each dimensional group. Line of glomeruli symmetry determined from 1D AP,ML heatmap mean of top 2 peaks Olfr features - class, tanzone, matsunami OE diffE, RTPdko OB scaffold with 2922 voxels, OR positions will only be assigned to these positions
kzY <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/input/covarintactchemo_over_samples_200923.csv", col_names = TRUE) %>% select(-X1) %>% filter(dimrep == 1 | dimrep == 2)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## name = col_character(),
## dim = col_character()
## )
## ℹ Use `spec()` for the full column specifications.ectopic <- c("Olfr287","Olfr32")
kzYgood <- dplyr::select(kzY, -ectopic) %>% dplyr::select(-name, -rep, -slice, -dim, -dimrep)## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(ectopic)` instead of `ectopic` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.dim(kzYgood) #1100 minus number of genes in ectopic## [1] 135 1482kzmY <- as.matrix(kzYgood)
rownames(kzmY) <- paste0("sample", 1:dim(kzY)[1])
#dimwise TPM plot
s1 <- kzY %>% select(slice, dim, dimrep, Olfr1377) %>% mutate(olfr = "1377") %>% rename("gene" = "Olfr1377")
s8 <- kzY %>% select(slice, dim, dimrep, Olfr881) %>% mutate(olfr = "881") %>% rename("gene" = "Olfr881")
scombo <- bind_rows(s1,s8)
ggplot(scombo) +
geom_line(aes(slice, gene, color = olfr)) +
facet_grid(vars(dimrep), vars(dim), scales = "free_y") +
ggtitle("TPM of Olfr881/1377 across dimensions and replicates")
#Examine Olfr881 TPM for all mice replicates from all dims
ggplot(kzY) + geom_line(aes(slice, Olfr881)) +
facet_grid(vars(dimrep), vars(dim), scales = "free_y") +
ggtitle("TPM of Olfr881 across dimensions and replicates")
#feature info
info <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/input/knowntanwavgFI.csv", col_names = TRUE) %>%
rename("olfrname" = "gene") %>%
select(olfrname:RTP, known, lowTPM)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## gene = col_character(),
## tan_zone = col_character(),
## oe_region = col_character(),
## RTP = col_character(),
## fisurface = col_logical(),
## ish_id = col_logical(),
## lacz_xg = col_logical(),
## fp_xg = col_logical(),
## med_glom = col_character(),
## lat_glom = col_character(),
## citation = col_character(),
## known = col_logical(),
## lowTPM = col_logical()
## )
## ℹ Use `spec()` for the full column specifications.#line of bulb symmetry as found using single-dimension heatmap data
#the average of the two calculated lines is used to call whether a predicted glomeruli position is medial or lateral
symline <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/input/symline.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## apvals = col_double(),
## mlvals = col_double()
## )ggplot() +
geom_blank() +
geom_abline(aes(slope = 0.32655, intercept = 7.61119, color = "rep1")) +
geom_abline(aes(slope = 0.24922, intercept = 8.83890, color = "rep2")) +
geom_abline(aes(slope = 0.287885, intercept = 8.225045, color = "mean")) +
xlim(0,23) +
ylim(0,22) +
ggtitle("Glomeruli symmetry line as calculated by single dimension heatmap peaks") +
xlab("Mean position of top 2 AP peaks") +
ylab("Mean position of top 2 ML peaks")
# Example 3D left OB for future orientation of dimensions
#blankdata <- Scat_rank("Olfr881", 1, "data") %>% select(AntPos:VenDor) %>% arrange(AntPos, MedLat, VenDor)
#write_csv(blankdata, "~/Desktop/rproj/obmap/allmice/v21_gen25/base_files/blankOBcoords.csv")
#blankOBcoords200922
blankdata <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/input/blankOBcoords200922.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## AntPos = col_double(),
## MedLat = col_double(),
## VenDor = col_double()
## )dvml_blank <- blankdata %>%
mutate(dimdv = ifelse(VenDor >= 13,
"Dorsal",
"Ventral"),
dimml = ifelse(MedLat > 17,
"Lateral",
ifelse(MedLat < 5,
"Medial",
NA)),
dim = ifelse(is.na(dimml), dimdv, dimml)) %>%
rowwise() %>%
mutate(symdim = ifelse(MedLat == round(symline$mlvals[which(symline$apvals == AntPos)]),"SymLine", dim)) %>%
ungroup()
#blankOB
plot_ly(dvml_blank, x = ~MedLat, y = ~AntPos, z = ~VenDor, color = ~VenDor,
text = ~paste('Dim: ', dim,
'<br>AP:', AntPos,
'<br>ML:', MedLat,
'<br>VD:', VenDor),
marker = list(size = 8, symbol = "circle"),
type = 'scatter3d',
mode = 'markers',
hoverinfo = "none",
hovertext="none") %>%
layout(scene = list(xaxis = list(title = 'Medial-Lateral',
backgroundcolor="rgb(200, 200, 230",
gridcolor="rgb(255,255,255)",
showbackground=TRUE,
zerolinecolor="rgb(255,255,255"),
yaxis = list(title = 'Anterior-Posterior',
backgroundcolor="rgb(200, 200, 230",
gridcolor="rgb(255,255,255)",
showbackground=TRUE,
zerolinecolor="rgb(255,255,255)"),
zaxis = list(title = 'Ventral-Dorsal',
backgroundcolor="rgb(200, 200, 230",
gridcolor="rgb(255,255,255)",
showbackground=TRUE,
zerolinecolor="rgb(255,255,255)")))#rankedolfr2 is a tidy_result ranked tibble using all intact olfr tpm
#rankedchemo is a tidy_result ranked tibble using all intact chemosensory gene tpm
#orbyorcor <- tibble(olfrname = ranked_olfr2$olfrname, orp50 = ranked_olfr2$p50, chemp50 = ranked_chemORs$p50) %>% group_by(olfrname) %>% summarise(cor = cor(orp50, chemp50)) %>% left_join(info, by = "olfrname")
orXor_cor <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/output/chemoVolfr_p50.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## olfrname = col_character(),
## cor = col_double(),
## class = col_double(),
## tan_zone = col_character(),
## tzsimple = col_double(),
## oe_region = col_character(),
## RTP = col_character(),
## known = col_logical(),
## lowTPM = col_logical()
## )ggplot(orXor_cor) + geom_violin(aes(oe_region, cor)) +
ggtitle("150iter, using all Chemo does not significantly alter voxel probability rankings")
#load tidy result and ranked
#load model output
tidy_result <- readRDS("~/Desktop/obmap/r_analysis/3dimOB/tidyresults/200i_allchemo_dimrep12_allvox.RDS")
ranked <- tidy_result %>%
arrange(gene) %>%
mutate(olfrname = rep(colnames(kzmY), each=max(tidy_result$voxel))) %>%
group_by(olfrname) %>%
mutate(voxrankperOR = rank(desc(p50))) %>%
ungroup() %>%
group_by(voxel) %>%
mutate(ORrankpervox = rank(desc(p50))) %>%
ungroup() %>% group_by(gene) %>%
mutate(allVoxAvgp50 = mean(p50)) %>%
ungroup() %>% group_by(gene, AntPos) %>%
mutate(AP_Avgp50 = mean(p50), AP_SNR = p50/AP_Avgp50) %>%
ungroup() %>% group_by(gene, MedLat) %>%
mutate(ML_Avgp50 = mean(p50), ML_SNR = p50/ML_Avgp50) %>%
ungroup() %>% group_by(gene, VenDor) %>%
mutate(VD_Avgp50 = mean(p50), VD_SNR = p50/VD_Avgp50) %>%
ungroup() %>%
mutate(voxSNRdim = (AP_SNR + ML_SNR + VD_SNR)/3) %>%
group_by(gene) %>%
mutate(geneRankSNR = rank(desc(voxSNRdim))) %>%
ungroup() %>% group_by(voxel) %>%
mutate(voxRankSNR = rank(desc(voxSNRdim))) %>%
ungroup() %>%
select(p2.5, p50, p97.5, AntPos:ORrankpervox, geneRankSNR,
voxRankSNR, AP_Avgp50:voxSNRdim, voxel)3D plots of OR probability as selected and constrained using my own functions
Plot X best voxels for an OR as ranked by SNR
Each voxel holds a probability for every OR, as determined by the expression of that ORs in the dimensional slices that intersect at that voxel. The total of the probability for all ORs in a voxel sums to 1. SNR is signal-to-noise ratio, essentially how much does an individual voxel stand out from the average probability of its 3 dimensional slices (all voxels at that single dimension position). Idea was to eliminate ectopics whose high expression across the whole OB dominated the voxel probability for nearly all voxels. 72 is ~2.5% of total voxel positions and presents a robust visualization, meaning that increasing this number typically results in adding neighboring voxels.
Scat_rank("Olfr881", 72, title = "Olfr881 - Top 72 SnR voxels")Scat_rank("Olfr1377", 72, title = "Olfr1377 - Top 72 SnR voxels")zapORs <- c("Olfr15", "Olfr16", "Olfr17", "Olfr155", "Olfr160", "Olfr1507")
#other zapiec & mombaerts labeled ORs
# Scat_rank("Olfr16",15)
# Scat_rank("Olfr15", 15)
# Scat_rank("Olfr17", 50)
# Scat_rank("Olfr160", 15)
# Scat_rank("Olfr155", 15)
# Scat_rank("Olfr1507", 30)Plot best X voxels based on raw p50 (probability)
Using raw probability here, its pretty much the same as SNR after removing ectopics.
#ranked %>% filter(olfrname == "Olfr160") %>% filter(MedLat < 3) %>% arrange(desc(p50)) %>% select(voxrankperOR, AntPos:VenDor)
p50plot("Olfr881", 72, title = "Olfr881 - Top 72 probability voxels")p50plot("Olfr1377", 72, title = "Olfr1377 - Top 72 probability voxels")# p50plot("Olfr16", 60)
# p50plot("Olfr17", 70)
# p50plot("Olfr15", 36)
# p50plot("Olfr160", 36)
# p50plot("Olfr155", 36)
# p50plot("Olfr1507", 36)Clustering top ranked points
Given a number of top probability ranked points, cluster spatial neighbors.
Cluster("Olfr881", 1000, minClustSize = 2, chooseOut = "plot", title = "Olfr881 - Clustered top 100 probability voxels")Cluster("Olfr1377", 125, minClustSize = 2, chooseOut = "plot", title = "Olfr1377 - Clustered top 100 probability voxels")# Cluster("Olfr16", chooseOut = 100, "plot")
# Cluster("Olfr17", chooseOut = 100, "plot")
# Cluster("Olfr15", chooseOut = 100, "plot")
# Cluster("Olfr160", chooseOut = 100, "plot")
# Cluster("Olfr155", chooseOut = 100, "plot")
# Cluster("Olfr1507", chooseOut = 100, "plot")Pick the best Medial and Lateral cluster
Plot best Medial and best Lateral cluster from the set of clusters across a symmetry line that is calculated using single dimension heatmap data For clarity, it is not picking the 2 best clusters but the best cluster from the medial half and the best cluster from the lateral half.
BestML("Olfr881", clustersPerHalfBulb = 1, title = "Olfr881 - Highest Probability Medial and Lateral Clusters")BestML("Olfr1377", clustersPerHalfBulb = 1, title = "Olfr1377 - Highest Probability Medial and Lateral Cluster")# BestML("Olfr16", clustersPerHalfBulb = 1)
# BestML("Olfr17", clustersPerHalfBulb = 1)
# BestML("Olfr15", clustersPerHalfBulb = 1)
# BestML("Olfr160", clustersPerHalfBulb = 1)
# BestML("Olfr155", clustersPerHalfBulb = 1)
# BestML("Olfr1507", clustersPerHalfBulb = 1)New Dorsal constraint function
DorsalML("Olfr881", title = "Olfr881 - OE constrained DV predictions")## [1] "Olfr881"
## [1] "NA 100"
## [1] "NA 200"
## [1] "NA 300"
## [1] "NA 400"
## [1] 500
## [1] 600DorsalML("Olfr1377", title = "Olfr1377 - OE constrained DV predictions")## [1] "Olfr1377"
## [1] 100Make predicted positions for all genes
genenames <- colnames(kzmY)
length(genenames)## [1] 1482#out <- ListDorML(genenames, chooseOut = "data")
#write_csv(out, "~/Desktop/obmap/r_analysis/3dimOB/output/allchemo_dornonor_ldML_201012.csv")
out <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/output/allchemo_dornonor_ldML_201012.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## olfrname = col_character(),
## side = col_character(),
## tan_zone = col_character(),
## oe_region = col_character(),
## RTP = col_character(),
## known = col_logical(),
## lowTPM = col_logical(),
## test = col_character()
## )
## ℹ Use `spec()` for the full column specifications.